With the rapid development of modern medical device applications such as MRI, PET and CT, the 3D medical visualization has been widely applied in the medical diagnosis, adjuvant therapy, surgical planning and other fields. The 3D medical visualization volume rendering technique has a very good application value and prospect because of its good “fidelity”. However, with significant improvement in the accuracy and resolution of the medical digital image, the amount of medical image data increases rapidly, its ultra-large-scale medical image data and ultra-large computational complexity became the bottlenecks of achieving the 3D medical volume rendering. At present, in order to meet the ultra-large computational complexity and multi-user interaction requirement of large-scale medical image data volume rendering, the studies in recent years have focused on parallel volume rendering, GPU-based hardware acceleration and efficient parallel computing of GPU embedded MapReduce. S. Eilemann, et al. designed the parallel volume rendering to meet the requirements of fast volume rendering. The data storage and processing based on CUDA (compute unified device architecture) designed by Dong Xianling from School of Biomedical Engineering, Southern Medical University, Qin Xujia from College of Computer Science & Technology, Zhejiang University of Technology, et al. achieve the parallel ray-casting algorithm. The computing framework based on MapReduce designed by Vo H T, et al. achieves the z-buffer rendering based on MapReduce, parallel mesh simplification and isosurface extraction and other basic graphics algorithms. Data storage and processing based on CUDA is one example of a parallel computing platform and programming model, but it is to be understood the following discussions referencing CUDA could apply to other types of parallel computing platforms and programming models as well.
Although the above-mentioned methods have reached a certain level of accelerated rendering effect, there are certain shortcomings and disadvantages, for example, single CUDA processing must be supported by the NVIDIA graphics card hardware, thereby increasing costs, and the stand-alone rendering restricts speeding up; furthermore, the computation of a large amount of duplicate data also increases the system processing load, thus wasting the system resources.